Research on human–AI decision-making (HAID) has grown rapidly across domains, yet the literature remains fragmented with conceptual ambiguities hindering systematic synthesis. We present a systematic review of HAID publications from 2016 to 2024, analyzing 1,493 articles from 20 publishers and databases. Our work contributes: (1) a systematic categorization including 6 contribution types, 13 domains of application, and 4 decision levels; (2) taxonomies of 8 AI roles and 6 human roles, their inputs to decision-making, and their influences on each other; and (3) the identification of 4 research gaps and pathways forward. This interactive interface provides access to our complete corpus, enabling exploration through semantic visualization, filtering, and search capabilities.
This visualization shows papers clustered by semantic similarity. Papers that are close together share similar concepts. Click on any paper in the table below will highlight the corresponding point in the visualization. Note: it might take a few moments to load the dataset.
Browse and filter the complete collection of papers. Click on column headers to sort, use the filters to narrow your search, and click on any row to highlight it in the visualization above.